参考verl对dapo的实现，首先咱们看一下入口.sh和.py文件，在./recipe/dapo/文件夹中有以下目录
.
├── config
│ ├── dapo_megatron_trainer.yaml
│ └── dapo_trainer.yaml
├── dapo_ray_trainer.py
├── main_dapo.py
├── prepare_dapo_data.sh
├── README.md
├── run_dapo_qwen2.5_32b.sh整体的执行顺序：
main_dapo.py：数据加载初始化、初始化actor_rollout model、rm model，加载reward_managerdapo_ray_trainer.py：RL训练流程- 对batch进行repeate，每个q采样n次
- 记录每个采样的log，以及对应的reward_score 和 advantage
- filter掉一个q的所有sample的score都是1或都是0，继续获取新的q进行采样，直到满足要求的batch的大小达到
train_prompt_bsz。（值得注意的是，batch大小是gen_prompt_bsz=3*train_prompt_bsz，通过提高采样q的个数，避免满足要求的q不到train_prompt_bsz）。
- filter掉一个q的所有sample的score都是1或都是0，继续获取新的q进行采样，直到满足要求的batch的大小达到
- 每mini_batch的data进行模型更新
- 每micro_batch的data进行前向传播（token-mean loss）与梯度计算
具体代码实例:
main_dapo.py
python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Note that we don't combine the main with ray_trainer as ray_trainer is used by other main.
"""
import os
import socket
import hydra
import ray
from omegaconf import OmegaConf
from verl.trainer.ppo.reward import load_reward_manager
from verl.utils.device import is_cuda_available
from .dapo_ray_trainer import RayDAPOTrainer
@hydra.main(config_path="config", config_name="dapo_trainer", version_base=None)
def main(config):
run_ppo(config)
#################################################################
# RL训练入口
#################################################################
def run_ppo(config) -> None:
if not ray.is_initialized():
# this is for local ray cluster
default_runtime_env = {
"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "WARN"}
}
ray_init_kwargs = config.ray_kwargs.get("ray_init", {})
runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {})
runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs)
ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env})
print(f"ray init kwargs: {ray_init_kwargs}")
ray.init(**OmegaConf.to_container(ray_init_kwargs))
try:
if (
is_cuda_available
and config.global_profiler.tool == "nsys"
and OmegaConf.select(config.global_profiler, "steps") is not None
and len(OmegaConf.select(config.global_profiler, "steps")) > 0
):
nsight_options = OmegaConf.to_container(
config.global_profiler.global_tool_config.nsys.controller_nsight_options
)
runner = TaskRunner.options(runtime_env={"nsight": nsight_options}).remote()
else:
runner = TaskRunner.remote()
ray.get(runner.run.remote(config))
finally:
if ray.is_initialized():
ray.shutdown()
@ray.remote(num_cpus=1) # please make sure main_task is not scheduled on head
class TaskRunner:
def run(self, config):
# print initial config
from pprint import pprint
from omegaconf import OmegaConf
from verl.utils.fs import copy_to_local
print(f"TaskRunner hostname: {socket.gethostname()}, PID: {os.getpid()}")
pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values
OmegaConf.resolve(config)
# download the checkpoint from hdfs
local_path = copy_to_local(config.actor_rollout_ref.model.path)
# instantiate tokenizer
from verl.utils import hf_processor, hf_tokenizer
tokenizer = hf_tokenizer(local_path)
processor = hf_processor(local_path, use_fast=True) # used for multimodal LLM, could be none
from verl.single_controller.ray import RayWorkerGroup
#################################################################
# 加载actor worker
#################################################################
# define worker classes
if config.actor_rollout_ref.actor.strategy in {"fsdp", "fsdp2"}:
assert config.critic.strategy in {"fsdp", "fsdp2"}
from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker
ray_worker_group_cls = RayWorkerGroup
elif config.actor_rollout_ref.actor.strategy == "megatron":
assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker
ray_worker_group_cls = RayWorkerGroup
else:
raise NotImplementedError
from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role
role_worker_mapping = {
Role.ActorRollout: ray.remote(ActorRolloutRefWorker),
Role.Critic: ray.remote(CriticWorker),
}
global_pool_id = "global_pool"
resource_pool_spec = {
global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes,
}
mapping = {
Role.ActorRollout: global_pool_id,
Role.Critic: global_pool_id,
}
# we should adopt a multi-source reward function here
# - for rule-based rm, we directly call a reward score
# - for model-based rm, we call a model
# - for code related prompt, we send to a sandbox if there are test cases
# - finally, we combine all the rewards together
# - The reward type depends on the tag of the data
if config.reward_model.enable:
if config.reward_model.strategy in {"fsdp", "fsdp2"}:
from verl.workers.fsdp_workers import RewardModelWorker
elif config.reward_model.strategy == "megatron":
from verl.workers.megatron_workers import RewardModelWorker
else:
raise NotImplementedError
role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker)
mapping[Role.RewardModel] = global_pool_id
# reference model
if config.algorithm.use_kl_in_reward or config.actor_rollout_ref.actor.use_kl_loss:
role_worker_mapping[Role.RefPolicy] = ray.remote(ActorRolloutRefWorker)
mapping[Role.RefPolicy] = global_pool_id
#################################################################
# 加载reward manager函数。用于根据data计算对应的reward score
#################################################################
reward_fn = load_reward_manager(
config,
tokenizer,
0,
max_resp_len=config.data.max_response_length,
overlong_buffer_cfg=config.reward_model.overlong_buffer,
)
# Note that we always use function-based RM for validation
val_reward_fn = load_reward_manager(
config,
tokenizer,
1,
max_resp_len=config.data.max_response_length,
overlong_buffer_cfg=config.reward_model.overlong_buffer,
)
resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)
#################################################################
# 加载主要的DAPO RL训练类，并运行.fit()
#################################################################
trainer = RayDAPOTrainer(
config=config,
tokenizer=tokenizer,
processor=processor,
role_worker_mapping=role_worker_mapping,
resource_pool_manager=resource_pool_manager,
ray_worker_group_cls=ray_worker_group_cls,
reward_fn=reward_fn,
val_reward_fn=val_reward_fn,
)
trainer.init_workers()
trainer.fit()
if __name__ == "__main__":
main()我们紧接着来看一下from verl.trainer.ppo.reward import load_reward_manager。
配置文件中verl/recipe/dapo/run_dapo_qwen2.5_32b.sh给出了reward的类型
shell
enable_overlong_buffer=True
overlong_buffer_len=$((1024 * 4)) # overlong soft
overlong_penalty_factor=1.0
reward_model.reward_manager=dapo \
reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
reward_model.overlong_buffer.len=${overlong_buffer_len} \
reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \verl.trainer.ppo.reward.py
python
def load_reward_manager(
config: DictConfig, tokenizer: Any, num_examine: int, **reward_kwargs: Any
) -> AbstractRewardManager:
"""
Load and initialize a reward manager based on the configuration.
Args:
config: PPO trainer configuration object containing reward_model fields.
tokenizer: Tokenizer object used for processing text.
num_examine: Number of samples to examine.
**reward_kwargs: Additional keyword arguments for the reward manager.
Returns:
An instance of the specified reward manager class.
"""
# Try to get a custom reward function based on the configuration
# user defined reward manager can be registered in custom_reward_fn
compute_score = get_custom_reward_fn(config)
final_compute_score = compute_score
# The list of pre-defined reward managers are defined in `verl/workers/reward_manager/`:
# naive: NaiveRewardManager
# prime: PrimeRewardManager
# batch: BatchRewardManager
# dapo: DAPORewardManager
# Note(haibin.lin): For custom reward managers, please make sure they are imported and
# registered via `verl.workers.reward_manager.register`
# By default reward_manager is set to naive (NaiveRewardManager)
#################################################################
# 在这里加载具体的reward_manager
#################################################################
reward_manager_name = config.reward_model.get("reward_manager", "naive")
reward_manager_cls = get_reward_manager_cls(reward_manager_name)
if compute_score is None:
sandbox_config = config.reward_model.get("sandbox_fusion")
sandbox_url = sandbox_config.get("url") if sandbox_config else None
memory_limit_mb = sandbox_config.get("memory_limit_mb", 1024)
if sandbox_url:
sandbox_manager = multiprocessing.Manager()
# Create a semaphore to control concurrent access to the sandbox
_concurrent_semaphore = sandbox_manager.Semaphore(sandbox_config.get("max_concurrent", 64))
final_compute_score = partial(
default_compute_score,
sandbox_fusion_url=sandbox_url,
concurrent_semaphore=_concurrent_semaphore,
memory_limit_mb=memory_limit_mb,
)
else:
final_compute_score = default_compute_score
#################################################################
# 这里的reward_manager_cls 其实是DAPO，
#################################################################
# Instantiate and return the reward manager with the specified parameters
return reward_manager_cls(
tokenizer=tokenizer,
num_examine=num_examine,
compute_score=final_compute_score,
reward_fn_key=config.data.reward_fn_key,
**reward_kwargs,
)这里需要知道dapo的reward_manager_cls 具体是什么，因为reward需要batch数据才能计算，因此对于reward manager咱们先按下不表（其实dapo对应的reward_manager_cls是在verl/verl/workers/reward_manager/dapo.py），先去dapo_ray_trainer.py看一下batch是怎么采样的，再回来仔细阅读reward的具体计算方法。
dapo_ray_trainer.py
python
#################################################################
# RayDAPOTrainer继承于RayPPOTrainer
# fit()函数：执行dapo的训练，包括（1）动态采样（2）overlong soft reward计算（3）token-level loss
#################################################################
class RayDAPOTrainer(RayPPOTrainer):
"""
Note that this trainer runs on the driver process on a single CPU/GPU node.
"""
def fit(self):
"""
The training loop of PPO.
The driver process only need to call the compute functions of the worker group through RPC
to construct the PPO dataflow.
The light-weight advantage computation is done on the driver process.
"""
from omegaconf import OmegaConf
from verl.utils.tracking import Tracking
logger = Tracking(
project_name=self.config.trainer.project_name,
experiment_name=self.config.trainer.experiment_name,
default_backend=self.config.trainer.logger,
config=OmegaConf.to_container(self.config, resolve=True),
)
self.global_steps = 0
self.gen_steps = 0
# load checkpoint before doing anything
self._load_checkpoint()
# perform validation before training
# currently, we only support validation using the reward_function.
if self.val_reward_fn is not None and self.config.trainer.get("val_before_train", True):
val_metrics = self._validate()
assert val_metrics, f"{val_metrics=}"
pprint(f"Initial validation metrics: {val_metrics}")
logger.log(data=val_metrics, step=self.global_steps)
if self.config.trainer.get("val_only", False):
return
if self.config.actor_rollout_ref.rollout.get("skip_rollout", False):
rollout_skip = RolloutSkip(self.config, self.actor_rollout_wg)
rollout_skip.wrap_generate_sequences()
# add tqdm
progress_bar = tqdm(total=self.total_training_steps, initial=self.global_steps, desc="Training Progress")
# we start from step 1
self.global_steps += 1
self.gen_steps += 1
last_val_metrics = None
prev_step_profile = False
curr_step_profile = (
self.global_steps in self.config.global_profiler.steps
if self.config.global_profiler.steps is not None
else False
)
next_step_profile = False
timing_raw = defaultdict(float)
batch = None
#################################################################
# num_prompt_in_batch：记录filter后，std不等于0的q的个数，当模型更新后重新赋值为0
# num_gen_batches: 记录当前使用了多少个gen_batch，当模型更新后重新赋值为0
#################################################################
num_prompt_in_batch = 0
num_gen_batches = 0
#################################################################
# 正式开始训练，循环每个epoch后，循环每个gen_batch
#################################################################
for epoch in range(self.config.trainer.total_epochs):
for batch_dict in self.train_dataloader:
metrics = {}
with marked_timer("start_profile", timing_raw):
self._start_profiling(
not prev_step_profile and curr_step_profile
if self.config.global_profiler.profile_continuous_steps
else curr_step_profile
)
#################################################################
# new_batch 是DataProto类型（具体见verl/verl/protocol.py），
# new_batch.batch是TensorDict类型
# new_batch中q的数量是可训练batch大小的3倍（增加采样的batch的q的个数）
#################################################################
new_batch: DataProto = DataProto.from_single_dict(batch_dict)
num_gen_batches += 1
# pop those keys for generation
if "multi_modal_data" in new_batch.non_tensor_batch.keys():
gen_batch = new_batch.pop(
batch_keys=["input_ids", "attention_mask", "position_ids"],
non_tensor_batch_keys=["raw_prompt_ids", "multi_modal_data"],
)
else:
# 从new_batch中提取对应的key，构建gen_batch
gen_batch = new_batch.pop(
batch_keys=["input_ids", "attention_mask", "position_ids"],
non_tensor_batch_keys=["raw_prompt_ids"],
)
# 这里为什么要repeate呢，因为每个prompt要采样n次，所以repeat n次。这里的interleave=True
# gen_batch: (bsz, response_length),
# gen_batch_output: (bsz*n, response_length)
gen_batch_output = gen_batch.repeat(
repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True
)
is_last_step = self.global_steps >= self.total_training_steps
with marked_timer("step", timing_raw):
# generate a batch
with marked_timer("gen", timing_raw, "red"):
gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch_output)
timing_raw.update(gen_batch_output.meta_info["timing"])
gen_batch_output.meta_info.pop("timing", None)
# 这个advatange 可以先忽略。RMAX需要先计算 贪心采样的sample的logits作为后序adv计算的baseline
if self.config.algorithm.adv_estimator == AdvantageEstimator.REMAX:
with marked_timer("gen_max", timing_raw, "red"):
gen_baseline_batch = deepcopy(gen_batch)
# 这里是贪心采样的baseline，do_sample = False
gen_baseline_batch.meta_info["do_sample"] = False
gen_baseline_output = self.actor_rollout_wg.generate_sequences(gen_baseline_batch)
new_batch = new_batch.union(gen_baseline_output)
# compute reward model score on new_batch
rm_scores = None
if self.use_rm and "rm_scores" not in new_batch.batch.keys():
rm_scores = self.rm_wg.compute_rm_score(new_batch)
new_batch = new_batch.union(rm_scores)
reward_baseline_tensor, _ = compute_reward(new_batch, self.reward_fn)
reward_baseline_tensor = reward_baseline_tensor.sum(dim=-1)
keys_to_pop = set(gen_baseline_output.batch.keys())
if rm_scores is not None:
keys_to_pop.update(rm_scores.batch.keys())
new_batch.pop(batch_keys=list(keys_to_pop))
new_batch.batch["reward_baselines"] = reward_baseline_tensor
del rm_scores, gen_baseline_batch, gen_baseline_output
#################################################################
# new_batch的大小是gen_prompt_bsz
# 对每一个prompt设置一个专属的标识 uid
# 之所以设置uid，是因为之后对sample计算reward时，需要对同一个q的n个sample的reward标准化
#################################################################
new_batch.non_tensor_batch["uid"] = np.array(
[str(uuid.uuid4()) for _ in range(len(new_batch.batch))], dtype=object
)
# 对batch中的每个key进行repeat（这里应该主要是对uid进行repeat）
# repeat to align with repeated responses in rollout
new_batch = new_batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True)
# 把采样完的放到new_batch中
new_batch = new_batch.union(gen_batch_output)
with marked_timer("reward", timing_raw, "yellow"):
# compute scores. Support both model and function-based.
# We first compute the scores using reward model. Then, we call reward_fn to combine
# the results from reward model and rule-based results.
if self.use_rm and "rm_scores" not in new_batch.batch.keys():
# we first compute reward model score
reward_tensor = self.rm_wg.compute_rm_score(new_batch)
new_batch = new_batch.union(reward_tensor)
# 计算new_batch各个采样的reward，根据设置好的self.reward_fn
# we combine with rule-based rm
reward_tensor, reward_extra_infos_dict = compute_reward(new_batch, self.reward_fn)
new_batch.batch["token_level_scores"] = reward_tensor
if reward_extra_infos_dict:
new_batch.non_tensor_batch.update(
{k: np.array(v) for k, v in reward_extra_infos_dict.items()}
)
# compute rewards. apply_kl_penalty if available
if self.config.algorithm.use_kl_in_reward:
new_batch, kl_metrics = apply_kl_penalty(
new_batch, kl_ctrl=self.kl_ctrl_in_reward, kl_penalty=self.config.algorithm.kl_penalty
)
metrics.update(
kl_metrics
) # TODO: This will be cleared if we use multiple genenration batches
else:
new_batch.batch["token_level_rewards"] = new_batch.batch["token_level_scores"]
#################################################################
# dapo的filter（dynamic sample）部分
#################################################################
if not self.config.algorithm.filter_groups.enable:
batch = new_batch
else: # NOTE: When prompts after filtering is less than train batch size,
# we skip to the next generation batch
metric_name = self.config.algorithm.filter_groups.metric
if metric_name == "seq_final_reward":
# Turn to numpy for easier filtering
new_batch.non_tensor_batch["seq_final_reward"] = (
new_batch.batch["token_level_rewards"].sum(dim=-1).numpy()
)
elif metric_name == "seq_reward":
new_batch.non_tensor_batch["seq_reward"] = (
new_batch.batch["token_level_scores"].sum(dim=-1).numpy()
)
# {uid: [r1,r2,r3,...,rn], uid: [...], ...}，记录每个轨迹所有采样的reward
# Collect the sequence reward for each trajectory
prompt_uid2metric_vals = defaultdict(list)
for uid, metric_val in zip(
new_batch.non_tensor_batch["uid"], new_batch.non_tensor_batch[metric_name], strict=True
):
prompt_uid2metric_vals[uid].append(metric_val)
# 每个q的reward的std
prompt_uid2metric_std = {}
for prompt_uid, metric_vals in prompt_uid2metric_vals.items():
prompt_uid2metric_std[prompt_uid] = np.std(metric_vals)
# 保留reward std不是0的q的uid
kept_prompt_uids = [
uid
for uid, std in prompt_uid2metric_std.items()
if std > 0 or len(prompt_uid2metric_vals[uid]) == 1
]
# 累积std不是0的q
num_prompt_in_batch += len(kept_prompt_uids)
# 记录留下来的q的sample的idx
kept_traj_idxs = []
for idx, traj_from_prompt_uid in enumerate(new_batch.non_tensor_batch["uid"]):
if traj_from_prompt_uid in kept_prompt_uids:
kept_traj_idxs.append(idx)
# 基于traj的id，检索对应的new_batch
new_batch = new_batch[kept_traj_idxs]
# batch是留下的traj数据的累积
batch = new_batch if batch is None else DataProto.concat([batch, new_batch])
# .sh文件配置的 可以训练的batch的最小大小（q的数量）
prompt_bsz = self.config.data.train_batch_size
# 如果现有的累积filter出来的q的数量小于 配置的最小数量，则continue继续使用下一个new_batch进行累积
if num_prompt_in_batch < prompt_bsz:
print(f"{num_prompt_in_batch=} < {prompt_bsz=}")
max_num_gen_batches = self.config.algorithm.filter_groups.max_num_gen_batches
# max_num_gen_batches是最多可以使用的gen_batch的个数
# 如果其小于0的话，即没有限制；若num_gen_batches < max_num_gen_batches则继续continue
if max_num_gen_batches <= 0 or num_gen_batches < max_num_gen_batches:
print(f"{num_gen_batches=}. Keep generating...")
self.gen_steps += 1
is_last_step = self.global_steps >= self.total_training_steps
continue
else:
raise ValueError(
f"{num_gen_batches=} >= {max_num_gen_batches=}."
+ " Generated too many. Please check if your data are too difficult."
+ " You could also try set max_num_gen_batches=0 to enable endless trials."
)
# 累积的符合的q个个数>=最小的可以训练的batch的大小
else:
# Align the batch
traj_bsz = self.config.data.train_batch_size * self.config.actor_rollout_ref.rollout.n
#################################################################
# 对齐一下，多余的轨迹会被抛弃，不知道会不会导致采样的利用效率不高，
# 会不会导致一些轨迹根本不会被训练到
#################################################################
batch = batch[:traj_bsz]
#################################################################
# actor模型更新
#################################################################
# === Updating ===
batch.batch["response_mask"] = compute_response_mask(batch)
# Balance the number of valid tokens across DP ranks.
# NOTE: This usually changes the order of data in the `batch`,
# which won't affect the advantage calculation (since it's based on uid),
# but might affect the loss calculation (due to the change of mini-batching).
# TODO: Decouple the DP balancing and mini-batching.
if self.config.trainer.balance_batch:
self._balance_batch(batch, metrics=metrics)
# compute global_valid tokens
batch.meta_info["global_token_num"] = torch.sum(batch.batch["attention_mask"], dim=-1).tolist()
#################################################################
# 记录filter后的batch的每个traj的采样时的logtis（token-level）
# 用于计算重要性采样的比值
#################################################################
# recompute old_log_probs
with marked_timer("old_log_prob", timing_raw, "blue"):
old_log_prob = self.actor_rollout_wg.compute_log_prob(batch)
entropys = old_log_prob.batch["entropys"]
response_masks = batch.batch["response_mask"]
loss_agg_mode = self.config.actor_rollout_ref.actor.loss_agg_mode
# 这里dapo的loss_agg_mode是"token_mean"
entropy_agg = agg_loss(loss_mat=entropys, loss_mask=response_masks, loss_agg_mode=loss_agg_mode)
old_log_prob_metrics = {"actor/entropy": entropy_agg.detach().item()}
metrics.update(old_log_prob_metrics)
old_log_prob.batch.pop("entropys")
batch = batch.union(old_log_prob)
if self.use_reference_policy:
# compute reference log_prob
with marked_timer("ref", timing_raw, "olive"):
ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch)
batch = batch.union(ref_log_prob)
# compute values
if self.use_critic:
with marked_timer("values", timing_raw, "cyan"):
values = self.critic_wg.compute_values(batch)
batch = batch.union(values)
# 计算token_level的重要性采样
# Compute rollout IS weights and mismatch metrics (inherited from RayPPOTrainer)
batch, is_metrics = self.compute_rollout_importance_weights_and_add_to_batch(batch)
# IS and mismatch metrics already have mismatch/ prefix
metrics.update(is_metrics)
#################################################################
# 计算advantage
#################################################################
with marked_timer("adv", timing_raw, "brown"):
# compute advantages, executed on the driver process
norm_adv_by_std_in_grpo = self.config.algorithm.get("norm_adv_by_std_in_grpo", True)
batch = compute_advantage(
batch,
adv_estimator=self.config.algorithm.adv_estimator,
gamma=self.config.algorithm.gamma,
lam=self.config.algorithm.lam,
num_repeat=self.config.actor_rollout_ref.rollout.n,
norm_adv_by_std_in_grpo=norm_adv_by_std_in_grpo,
)
# update critic
if self.use_critic:
with marked_timer("update_critic", timing_raw, "pink"):
critic_output = self.critic_wg.update_critic(batch)
critic_output_metrics = reduce_metrics(critic_output.meta_info["metrics"])
metrics.update(critic_output_metrics)
# implement critic warmup
if self.config.trainer.critic_warmup <= self.global_steps:
#################################################################
# 更新actor model（batch的大小是train_prompt_size）
# 每个mini_bsz 更新一次模型（参数-累积梯度）
# 每个micro_bsz 累积一次梯度
#################################################################
# update actor
with marked_timer("update_actor", timing_raw, "red"):
actor_output = self.actor_rollout_wg.update_actor(batch)
actor_output_metrics = reduce_metrics(actor_output.meta_info["metrics"])
metrics.update(actor_output_metrics)
# Log rollout generations if enabled
rollout_data_dir = self.config.trainer.get("rollout_data_dir", None)
if rollout_data_dir:
self._log_rollout_data(batch, reward_extra_infos_dict, timing_raw, rollout_data_dir)
# validate
if (
self.val_reward_fn is not None
and self.config.trainer.test_freq > 0
and (is_last_step or self.global_steps % self.config.trainer.test_freq == 0)
):
with marked_timer("testing", timing_raw, "green"):
val_metrics: dict = self._validate()
if is_last_step:
last_val_metrics = val_metrics
metrics.update(val_metrics)
if self.config.trainer.save_freq > 0 and (
is_last_step or self.global_steps % self.config.trainer.save_freq == 0
):
with marked_timer("save_checkpoint", timing_raw, "green"):
self._save_checkpoint()
with marked_timer("stop_profile", timing_raw):
next_step_profile = (
self.global_steps + 1 in self.config.global_profiler.steps
if self.config.global_profiler.steps is not None
else False
)
self._stop_profiling(
curr_step_profile and not next_step_profile
if self.config.global_profiler.profile_continuous_steps
else curr_step_profile
)
prev_step_profile = curr_step_profile
curr_step_profile = next_step_profile
# collect metrics
metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic))
metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw))
# TODO: implement actual tflpo and theoretical tflpo
n_gpus = self.resource_pool_manager.get_n_gpus()
metrics.update(compute_throughout_metrics(batch=batch, timing_raw=timing_raw, n_gpus=n_gpus))
timing_raw = defaultdict(float) # clear timing
metrics["train/num_gen_batches"] = num_gen_batches
batch = None
num_prompt_in_batch = 0
num_gen_batches = 0
# TODO: make a canonical logger that supports various backend
logger.log(data=metrics, step=self.global_steps)
if is_last_step:
pprint(f"Final validation metrics: {last_val_metrics}")
progress_bar.close()
return
progress_bar.update(1)
self.global_steps += 1
self.gen_steps += 1
# check if last step checkpint exists
checkpoint_dir = os.path.join(self.config.trainer.default_local_dir, f"global_step_{self.global_steps}")
if not os.path.exists(checkpoint_dir):
# save last step checkpoint
timing_raw = defaultdict(float)
with marked_timer("save_checkpoint", timing_raw, "green"):
self._save_checkpoint()
metrics = {f"timing/{k}": v for k, v in timing_raw.items()}
logger.log(data=metrics, step=self.global_steps)这时候咱们再看一下dapo的reward manager实现：主要和ppo的区别在于使用了overlong_buffer，计算长度的reward
verl/verl/workers/reward_manager/dapo.py
python
#################################################################
# 这里使用dapo注册了DAPORewardManager，因此可以用
# reward_manager_cls = get_reward_manager_cls(reward_manager_name)得到
#################################################################
@register("dapo")
class DAPORewardManager(AbstractRewardManager):
"""The reward manager."""
def __init__(
self,
tokenizer,
num_examine,
compute_score=None,
reward_fn_key="data_source",
max_resp_len=None,
overlong_buffer_cfg=None,
) -> None:
self.tokenizer = tokenizer
self.num_examine = num_examine # the number of batches of decoded responses to print to the console
self.compute_score = compute_score or default_compute_score
self.reward_fn_key = reward_fn_key
self.overlong_buffer_cfg = overlong_buffer_cfg
self.max_resp_len = max_resp_len
if self.overlong_buffer_cfg is not None:
assert self.max_resp_len is not None, (
f"max_resp_len must be provided if {overlong_buffer_cfg=}, but got None"
)
assert self.max_resp_len >= self.overlong_buffer_cfg.len, (
"max_resp_len must be larger than overlong_buffer.len"
)
#################################################################
# DAPO reward manager的主要函数
#################################################################
def __call__(self, data: DataProto, return_dict: bool = False):
"""We will expand this function gradually based on the available datasets"""
# If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn
if "rm_scores" in data.batch.keys():
if return_dict:
reward_extra_keys = data.meta_info.get("reward_extra_keys", [])
reward_extra_info = {key: data.non_tensor_batch[key] for key in reward_extra_keys}
return {"reward_tensor": data.batch["rm_scores"], "reward_extra_info": reward_extra_info}
else:
return data.batch["rm_scores"]
reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
reward_extra_info = defaultdict(list)
already_print_data_sources = {}
for i in range(len(data)):
data_item = data[i] # DataProtoItem
prompt_ids = data_item.batch["prompts"]
prompt_length = prompt_ids.shape[-1]
########################################################
# 值得注意的是。prompt_ids是左填充的
# response_ids是右填充的
########################################################
valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum()
valid_prompt_ids = prompt_ids[-valid_prompt_length:]
response_ids = data_item.batch["responses"]
valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum()
valid_response_ids = response_ids[:valid_response_length]
# decode
prompt_str = self.tokenizer.decode(valid_prompt_ids, skip_special_tokens=True)
response_str = self.tokenizer.decode(valid_response_ids, skip_special_tokens=True)
eos_token = self.tokenizer.eos_token
if response_str.endswith(eos_token):
response_str = response_str[: -len(eos_token)]
ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"]
data_source = data_item.non_tensor_batch[self.reward_fn_key]
extra_info = data_item.non_tensor_batch.get("extra_info", {})
rollout_reward_scores = data_item.non_tensor_batch.get("reward_scores", {})
extra_info["rollout_reward_scores"] = rollout_reward_scores
result = self.compute_score(
data_source=data_source,
solution_str=response_str,
ground_truth=ground_truth,
extra_info=extra_info,
)
score: float
if isinstance(result, dict):
score = result["score"]
# Store the information including original reward
for key, value in result.items():
reward_extra_info[key].append(value)
else:
score = result
reward_extra_info["acc"].append(score)
reward = score
########################################################
# 这里是overlong reward的计算
########################################################
if self.overlong_buffer_cfg.enable:
overlong_buffer_len = self.overlong_buffer_cfg.len
expected_len = self.max_resp_len - overlong_buffer_len
exceed_len = valid_response_length - expected_len
overlong_penalty_factor = self.overlong_buffer_cfg.penalty_factor
overlong_reward = min(-exceed_len / overlong_buffer_len * overlong_penalty_factor, 0)
reward += overlong_reward
if self.overlong_buffer_cfg.log:
reward_extra_info["overlong_reward"].append(overlong_reward)
reward_extra_info["overlong"].append(overlong_reward < 0)
reward_tensor[i, valid_response_length - 1] = reward
if data_source not in already_print_data_sources:
already_print_data_sources[data_source] = 0
if already_print_data_sources[data_source] < self.num_examine:
already_print_data_sources[data_source] += 1
print("[prompt]", prompt_str)
print("[response]", response_str)
print("[ground_truth]", ground_truth)
if isinstance(result, dict):
for key, value in result.items():
print(f"[{key}]", value)
else:
print("[score]", score)
if return_dict:
return {
"reward_tensor": reward_tensor,
"reward_extra_info": reward_extra_info,
}
else:
return reward_tensordapo和ppo的具体区别可进一步参考：dapo readme